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Cluster Computing
Published in: Cluster Computing 5/2019

06-02-2018

Short-term load forecasting based on multivariate time series prediction and weighted neural network with random weights and kernels

Authors: Kun Lang, Mingyuan Zhang, Yongbo Yuan, Xijian Yue

Published in: Cluster Computing | Special Issue 5/2019

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Abstract

Forecasting short-term load is a basic but indispensable problem for power system operations. This paper treats the forecasting problem as a multivariate time series forecasting problem. The electricity load and the corresponding temperature data are analyzed as correlative time series, and are reconstructed to the multivariate phase space. A neural network with random weights and kernels, which combines the advantages of the neural network and support vector machine including simple training and good generalization performance, is used as the forecasting model. Then, in order to further improve the forecasting performance, different weights are applied to the input data in the phase space according to the predictive value, and the resulting model is called weighted neural network with random weights and kernels. Simulation results based on the real world data set from the EUNITE competition show the effectiveness of the proposed method.
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Metadata
Title
Short-term load forecasting based on multivariate time series prediction and weighted neural network with random weights and kernels
Authors
Kun Lang
Mingyuan Zhang
Yongbo Yuan
Xijian Yue
Publication date
06-02-2018
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 5/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1685-7

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